On-Board Tailpipe Particulate Number Modeling AUTHORS: Chen Zhang, Lisa Aultman-Hall, Britt Holmén, Eric Jackson

ABSTRACT This study focused on assessing relationships between

tailpipe particulate numbers (PN) and second-by-second vehicle operating characteristics, including

speed, acceleration, vehicle specific power (VSP) etc. This study represents an advance in terms of detail over typical emission studies in that a PN prediction model is

estimated based on the continuous real-world PN data collected at a second-by-second level. The results of

this study contribute efforts for a new generation transportation emission models including movement towards inclusion of particle number in EPA's MOVES

model.

ACKNOWLEDGMENTS This study was funded by National Science Foundation

(NSF). It is a continuation of prior studies completed at the University of Connecticut by Eric Jackson, Lisa Aultman-Hall, and Britt Holmén.

DATA

ANALYSIS

UNIVERSITY OF VERMONT TRANSPORTATION RESEARCH CENTER BURLINGTON, VERMONT www.uvm.edu/~transctr

NormPN Speed bin VSP bin Mode

NormPN 10.40932 0.73721 0.08841

<.0001 <.0001 <.0001

Speed bin 10.25848 0.49907

<.0001 <.0001

VSP bin 10.08773

<.0001

Mode 1

ScanTool

Video Camera

Tailpipe Adapter

5-Gas

Analyzer

Pitot Tube

Mini-Diluter

Condensation

Particle

Counter

GPS Receivers

Accelerometer

Desktop

Computer

Thermocouples

4 Pressure Sensors to Calculate

Exhaust Flow rate

Ambient Temperature

and Relative Humidity

Sensor

Average normalized PN emissions by location

CONCLUSIONS • Vehicle specific power (VSP) is the most relevant factor when predicting PN rate

• Different modes (accelerating, decelerating, cruising, and idling) corresponds to distinctive relationships between PN rate and VSP

• Fixed effect ANOVA models seem to provide better prediction results than continuous models • Model diagnostics results show better model results are obtained by vehicle mode • Potential future research direction – 1) spatial analysis for PN prediction; 2) assessing the

relationship between instrumentation accuracy, time resolution and model accuracy

Figure 2 PN rate compared to VSP on a Loge scale

Figure 1 PN rate compared to speed and accelerations (for one randomly selected run)

Figure 3 Plot of Normalized PN Rate vs. VSP by Mode

Table 1 Spearman Correlation Mix Figure 4 Normalized PN estimates versus VSP Bin by mode

Figure 5 Residual vs. VSP plots a. Model of all modes together b. Model of accelerating mode

Figure 1 PN rate compared to speed and accelerations (for one randomly selected run)